You are here

RAL Seminar Series Thursday, June 13, 201310:30 a.m. FL2-1022 Curtis Walker Graduate Research Assistant, University of Nebraska, Lincoln Intern, SOARS program Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with winter road maintenance. Pavement temperature is an important variable when considering vehicle response to various weather conditions. Tire friction is a complex function of tire and pavement temperatures. Furthermore, the frictional properties of different road surfaces are also dependent on the pavement temperature and should be considered. In general, it has been shown that as tire temperature increases friction decreases, affecting vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. During a summer research experience with the UCAR-SOARS Program work began to improve the forecasts by determining how cloud type impacts the amount of solar radiation reaching the surface and subsequent pavement temperatures. Cloud type information was obtained from the Naval Research Laboratory Cloud Classifier algorithm (NRLCC) and radiation data were obtained from a Davis Weather Station. A theoretical maximum solar radiation distribution was calculated. For each cloud type, the difference between the observed radiation at the surface and the theoretical top-of-the atmosphere radiation was computed and expressed as a percent reduction. Cloud type-radiation distribution analyses from Salisbury, North Carolina during May-June 2012 indicate that low clouds allow approximately 20% of the maximum possible radiation to reach the surface, mid-level clouds 32%, high clouds 40% and cumuliform types 34%. A categorical regression analysis revealed 33% of the variation in solar radiation on cloudy case days can be explained by cloud type. Inclusion of clear case days with apparent variability lowered the cloud type explanatory variable to 7% suggesting another influence on radiation. Ongoing thesis research continues to address the cloud-radiation forecasting problem for pavement temperature energy balance models. First, the influence of clouds on surface radiation measurements and the reliability of cloud detection from the NRLCC will be determined using the Great Plains as an initial study region due to a dense network of surface radiation observations from the High Plains Regional Climate Center and the Oklahoma Mesonet. Cloud properties and average radiation quantities will also be obtained from the NASA/GEWEX Surface Radiation Budget (SRB) in addition to CERES and MODIS satellite products. Radiative transfer relationships and SRB/satellite data will be used to isolate the cloud radiation signal. Statistical analyses will help determine which cloud properties have the most influence on surface radiation. These data will be used to develop a cloud radiation scheme that can be incorporated into EBMs and apply adjustments to the radiation forecast component. The next step will be to quantify the specific impact of the radiation on pavement temperatures. With a detailed case study analysis, the goal is to determine how specific pavement types respond to radiation variability induced by clouds and the importance of cloud cover duration. This work will refine the initial cloud radiation scheme to account for pavement response. The final step, a model sensitivity study will determine how well the cloud radiation scheme improves the forecasted road pavement temperatures when compared to actual observations. This sensitivity study will be conducted in various atmospheric conditions and seasons in the Great Plains to test the merit of the cloud radiation scheme for improving pavement temperature forecasts.